KAUST held a "Run for a Cure" charity race on October 28 for breast cancer research, with over 425 participants from KAUST and partner organizations. A KAUST Ph.D. student discussed her research on non-invasive early cancer detection using plasma blood samples. The event included 10K, 5K, and 3K runs through KAUST, aligning with Vision 2030's goal of increasing public participation in sports. Why it matters: This event highlights KAUST's commitment to healthcare research, community engagement, and supporting national goals for health and sustainability.
KAUST organized a breast cancer awareness event in Thuwal on October 12, attended by over 150 women and girls from the local community, along with healthcare and education partners. The event featured educational lectures, personal stories from breast cancer survivors, and interactive sessions on early screening. KAUST's director of Social and Community Development highlighted the university's commitment to women's health and empowerment through such initiatives. Why it matters: This event demonstrates KAUST's commitment to social responsibility and community engagement by promoting health awareness and empowering women, aligning with Saudi Vision 2030.
MBZUAI researchers are refining AI techniques to improve cancer diagnosis for colorectal and breast cancer, both common in the Middle East. They are using "few-shot tissue image generation," in which AI generates data for training AI models to recognize lesions, addressing the challenge of limited training data. The developed framework improves the efficiency of radiologists in breast cancer diagnosis, leading to better detection of breast lesions and timely treatment interventions. Why it matters: These advancements in AI-aided diagnostics can lead to earlier and more accurate cancer detection, ultimately improving patient outcomes in the region and beyond.
Petar Stojanov from the Broad Institute of MIT and Harvard will give a talk on cancer data analysis, covering the fundamentals of cancer, the nature of large-scale data collected, and main analysis objectives. The talk will also address open questions in cancer data analysis and how machine learning and generative modeling can help. Stojanov's research focuses on applying machine learning to genomic analysis of cancer mutation and single-cell RNA sequencing data. Why it matters: Applying AI and machine learning to cancer research can lead to a better understanding of the disease and development of new therapies.
MBZUAI master's student Sayed Hashim is applying machine learning to improve cancer diagnosis and treatment, motivated by personal loss. He and fellow student Muhammad Ali developed algorithms for cancer type classification from multi-omics data, achieving over 96% accuracy. Their work, supervised by MBZUAI faculty, resulted in a published paper on multi-omics data representation learning. Why it matters: This research demonstrates the potential of AI and machine learning to advance cancer research and personalized medicine in the region.
MBZUAI's BioMedIA lab, led by Mohammad Yaqub, is developing AI solutions for healthcare challenges in cardiology, pulmonology, and oncology using computer vision. Yaqub's previous research analyzed fetal ultrasound images to correlate bone development with maternal vitamin D levels. The lab is now applying image analysis to improve the treatment of head and neck cancer using PET and CT scans. Why it matters: This research demonstrates the potential of AI and computer vision to improve diagnostic accuracy and accessibility of healthcare in the region and beyond.
This article discusses the use of artificial intelligence in precision oncology, particularly in understanding individual tumor mechanisms and aiding clinical decision-making. Dr. Xinghua Lu, with extensive experience in medicine and biomedical informatics, will present research on individualized Bayesian causal inference methods for investigating oncogenic mechanisms. These methods aim to provide clinical decision support at the cellular, tumor, and patient levels. Why it matters: AI-driven precision oncology can enable more personalized and effective cancer treatments, improving patient outcomes in the region and globally.
Extreme E racing series is collaborating with KAUST and the Ba'a Foundation to conserve endangered turtles in Saudi Arabia. Rising sea levels have led to a 90% mortality rate of turtle eggs in 2019, threatening the already endangered species. The collaboration aims to protect turtle nesting sites along the Red Sea coastline. Why it matters: This initiative highlights the potential for partnerships between sports, academia, and conservation organizations to address climate change impacts on vulnerable ecosystems in the region.